class_docstr: General class for handling a Gaussian Process in GPyOpt.
  
  :param kernel: GPy kernel to use in the GP model.
  :param noise_var: value of the noise variance if known.
  :param exact_feval: whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios (default, False).
  :param optimizer: optimizer of the model. Check GPy for details.
  :param max_iters: maximum number of iterations used to optimize the parameters of the model.
  :param optimize_restarts: number of restarts in the optimization.
  :param sparse: whether to use a sparse GP (default, False). This is useful when many observations are available.
  :param num_inducing: number of inducing points if a sparse GP is used.
  :param verbose: print out the model messages (default, False).
  :param ARD: whether ARD is used in the kernel (default, False).
  :param mean_function: GPy Mapping to use as the mean function for the GP model (default, None).
  
  .. Note:: This model does Maximum likelihood estimation of the hyper-parameters.
docstr: Predicts the covariance matric for points in X.
  
  Parameters:
      X (np.ndarray) - points to run the prediction for.
      with_noise (bool) - whether to add noise to the prediction. Default is True.
docstr_tok: ["Predicts","the","covariance","matric","for","points","in","X.","Parameters",":","X","(","np.ndarray",")","-","points","to","run","the","prediction","for",".","with_noise","(","bool",")","-","whether","to","add","noise","to","the","prediction",".","Default","is","True","."]
code: def predict_covariance(self, X, with_noise=True):
      """
      Predicts the covariance matric for points in X.
  
      Parameters:
          X (np.ndarray) - points to run the prediction for.
          with_noise (bool) - whether to add noise to the prediction. Default is True.
      """
      _, v = self._predict(X, True, with_noise)
      return v
  
imports: import numpy
  import GPy
  from .base import BOModel
label_desc: Use image node_id 7 for calling the GPModel obj's underlying member method code with example usage: obj.predict_covariance(X, with_noise) and returns: v